A linear adaptive algorithm for data fusion in distributed detection systems

In this work we propose an adaptive fusion procedure to perform data detection in distributed detection systems with a decode-then-fuse type of receiver. Differently from previously proposed fusion rules, where nodes statistics (e.g. miss detection, false alarm and a priori probabilities) and channel transition probabilities are generally needed, the proposed non-assisted adaptive fusion algorithm adapts its coefficients and decision threshold based only on the received signals, so that time-varying or inhomogeneous distributed detection systems are well suited. Computer simulations show that the proposed adaptive fusion strategy delivers a performance very close to the optimal fusion rule.